


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import OwlViTImageProcessor, OwlViTProcessor







@require_vision
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							tempfile.mkdtemp()

						# fmt: off
						UpperCamelCase__       =							["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
						# fmt: on
						UpperCamelCase__       =							dict(zip(snake_case   ,    range(len(snake_case  )  )  )  )
						UpperCamelCase__       =							["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
						UpperCamelCase__       =							{"unk_token": "<unk>"}

						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["vocab_file"]  )
						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["merges_file"]  )
						with open(self.vocab_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write(json.dumps(snake_case  ) + "\n"  )
						with open(self.merges_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write("\n".join(snake_case  )  )

						UpperCamelCase__       =							{
						    "do_resize": True,
						    "size": 20,
						    "do_center_crop": True,
						    "crop_size": 18,
						    "do_normalize": True,
						    "image_mean": [0.48145466, 0.4578275, 0.40821073],
						    "image_std": [0.26862954, 0.26130258, 0.27577711],
						}
						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    snake_case  )
						with open(self.image_processor_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									json.dump(snake_case   ,    snake_case  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						return CLIPTokenizer.from_pretrained(self.tmpdirname   ,    pad_token="!"   ,    **snake_case  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,    pad_token="!"   ,    **snake_case  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						return OwlViTImageProcessor.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						shutil.rmtree(self.tmpdirname  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[np.random.randint(255   ,    size=(3, 30, 400)   ,    dtype=np.uinta  )]

						UpperCamelCase__       =							[Image.fromarray(np.moveaxis(snake_case   ,    0   ,    -1  )  ) for x in image_inputs]

						return image_inputs




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_tokenizer()
						UpperCamelCase__       =							self.get_rust_tokenizer()
						UpperCamelCase__       =							self.get_image_processor()

						UpperCamelCase__       =							OwlViTProcessor(tokenizer=snake_case   ,    image_processor=snake_case  )
						processor_slow.save_pretrained(self.tmpdirname  )
						UpperCamelCase__       =							OwlViTProcessor.from_pretrained(self.tmpdirname   ,    use_fast=snake_case  )

						UpperCamelCase__       =							OwlViTProcessor(tokenizer=snake_case   ,    image_processor=snake_case  )
						processor_fast.save_pretrained(self.tmpdirname  )
						UpperCamelCase__       =							OwlViTProcessor.from_pretrained(self.tmpdirname  )

						self.assertEqual(processor_slow.tokenizer.get_vocab()   ,    tokenizer_slow.get_vocab()  )
						self.assertEqual(processor_fast.tokenizer.get_vocab()   ,    tokenizer_fast.get_vocab()  )
						self.assertEqual(tokenizer_slow.get_vocab()   ,    tokenizer_fast.get_vocab()  )
						self.assertIsInstance(processor_slow.tokenizer   ,    snake_case  )
						self.assertIsInstance(processor_fast.tokenizer   ,    snake_case  )

						self.assertEqual(processor_slow.image_processor.to_json_string()   ,    image_processor.to_json_string()  )
						self.assertEqual(processor_fast.image_processor.to_json_string()   ,    image_processor.to_json_string()  )
						self.assertIsInstance(processor_slow.image_processor   ,    snake_case  )
						self.assertIsInstance(processor_fast.image_processor   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							OwlViTProcessor(tokenizer=self.get_tokenizer()   ,    image_processor=self.get_image_processor()  )
						processor.save_pretrained(self.tmpdirname  )

						UpperCamelCase__       =							self.get_tokenizer(bos_token="(BOS)"   ,    eos_token="(EOS)"  )
						UpperCamelCase__       =							self.get_image_processor(do_normalize=snake_case  )

						UpperCamelCase__       =							OwlViTProcessor.from_pretrained(
						    self.tmpdirname   ,    bos_token="(BOS)"   ,    eos_token="(EOS)"   ,    do_normalize=snake_case  )

						self.assertEqual(processor.tokenizer.get_vocab()   ,    tokenizer_add_kwargs.get_vocab()  )
						self.assertIsInstance(processor.tokenizer   ,    snake_case  )

						self.assertEqual(processor.image_processor.to_json_string()   ,    image_processor_add_kwargs.to_json_string()  )
						self.assertIsInstance(processor.image_processor   ,    snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()

						UpperCamelCase__       =							OwlViTProcessor(tokenizer=snake_case   ,    image_processor=snake_case  )

						UpperCamelCase__       =							self.prepare_image_inputs()

						UpperCamelCase__       =							image_processor(snake_case   ,    return_tensors="np"  )
						UpperCamelCase__       =							processor(images=snake_case   ,    return_tensors="np"  )

						for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum()   ,    input_processor[key].sum()   ,    delta=1E-2  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()

						UpperCamelCase__       =							OwlViTProcessor(tokenizer=snake_case   ,    image_processor=snake_case  )

						UpperCamelCase__       =							"lower newer"

						UpperCamelCase__       =							processor(text=snake_case   ,    return_tensors="np"  )

						UpperCamelCase__       =							tokenizer(snake_case   ,    return_tensors="np"  )

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key][0].tolist()   ,    encoded_processor[key][0].tolist()  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()

						UpperCamelCase__       =							OwlViTProcessor(tokenizer=snake_case   ,    image_processor=snake_case  )

						UpperCamelCase__       =							"lower newer"
						UpperCamelCase__       =							self.prepare_image_inputs()

						UpperCamelCase__       =							processor(text=snake_case   ,    images=snake_case  )

						self.assertListEqual(list(inputs.keys()  )   ,    ["input_ids", "attention_mask", "pixel_values"]  )

						# test if it raises when no input is passed
						with pytest.raises(snake_case  ):
									processor()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"google/owlvit-base-patch32"
						UpperCamelCase__       =							OwlViTProcessor.from_pretrained(snake_case  )

						UpperCamelCase__       =							["cat", "nasa badge"]
						UpperCamelCase__       =							processor(text=snake_case  )

						UpperCamelCase__       =							16
						self.assertListEqual(list(inputs.keys()  )   ,    ["input_ids", "attention_mask"]  )
						self.assertEqual(inputs["input_ids"].shape   ,    (2, seq_length)  )

						# test if it raises when no input is passed
						with pytest.raises(snake_case  ):
									processor()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"google/owlvit-base-patch32"
						UpperCamelCase__       =							OwlViTProcessor.from_pretrained(snake_case  )

						UpperCamelCase__       =							[["cat", "nasa badge"], ["person"]]
						UpperCamelCase__       =							processor(text=snake_case  )

						UpperCamelCase__       =							16
						UpperCamelCase__       =							len(snake_case  )
						UpperCamelCase__       =							max([len(snake_case  ) for texts in input_texts]  )

						self.assertListEqual(list(inputs.keys()  )   ,    ["input_ids", "attention_mask"]  )
						self.assertEqual(inputs["input_ids"].shape   ,    (batch_size * num_max_text_queries, seq_length)  )

						# test if it raises when no input is passed
						with pytest.raises(snake_case  ):
									processor()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							"google/owlvit-base-patch32"
						UpperCamelCase__       =							OwlViTProcessor.from_pretrained(snake_case  )

						UpperCamelCase__       =							["cat", "nasa badge"]
						UpperCamelCase__       =							processor(text=snake_case  )

						UpperCamelCase__       =							16
						UpperCamelCase__       =							inputs["input_ids"]
						UpperCamelCase__       =							[
						    [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						    [49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						]

						self.assertListEqual(list(inputs.keys()  )   ,    ["input_ids", "attention_mask"]  )
						self.assertEqual(inputs["input_ids"].shape   ,    (2, seq_length)  )
						self.assertListEqual(list(input_ids[0]  )   ,    predicted_ids[0]  )
						self.assertListEqual(list(input_ids[1]  )   ,    predicted_ids[1]  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()

						UpperCamelCase__       =							OwlViTProcessor(tokenizer=snake_case   ,    image_processor=snake_case  )

						UpperCamelCase__       =							self.prepare_image_inputs()
						UpperCamelCase__       =							self.prepare_image_inputs()

						UpperCamelCase__       =							processor(images=snake_case   ,    query_images=snake_case  )

						self.assertListEqual(list(inputs.keys()  )   ,    ["query_pixel_values", "pixel_values"]  )

						# test if it raises when no input is passed
						with pytest.raises(snake_case  ):
									processor()







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.get_image_processor()
						UpperCamelCase__       =							self.get_tokenizer()

						UpperCamelCase__       =							OwlViTProcessor(tokenizer=snake_case   ,    image_processor=snake_case  )

						UpperCamelCase__       =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCamelCase__       =							processor.batch_decode(snake_case  )
						UpperCamelCase__       =							tokenizer.batch_decode(snake_case  )

						self.assertListEqual(snake_case   ,    snake_case  )






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						super().__init__()
						self.register_modules(unet=snake_case   ,    scheduler=snake_case  )







			@torch.no_grad()
			def __call__(				self   ,    snake_case = 1   ,    snake_case = None   ,    snake_case = 50   ,    snake_case = "pil"   ,    snake_case = True   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.randn(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,    generator=snake_case   ,    )
						UpperCamelCase__       =							image.to(self.device  )

						# set step values
						self.scheduler.set_timesteps(snake_case  )

						for t in self.progress_bar(self.scheduler.timesteps  ):
									# 1. predict noise model_output
									UpperCamelCase__       =							self.unet(snake_case   ,    snake_case  ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__       =							self.scheduler.step(snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						UpperCamelCase__       =							(image / 2 + 0.5).clamp(0   ,    1  )
						UpperCamelCase__       =							image.cpu().permute(0   ,    2   ,    3   ,    1  ).numpy()
						if output_type == "pil":
									UpperCamelCase__       =							self.numpy_to_pil(snake_case  )

						if not return_dict:
									return (image,), "This is a local test"

						return ImagePipelineOutput(images=snake_case  ), "This is a local test"


